# Artificial Intelligence applications

Beijing Municipal Health Big Data and Policy Research Center and Yidu Tech signed a cooperation agreement during the 2026 China International Fair for Trade in Services to build a joint laboratory for intelligent clinical research. The two sides combine Beijing's health-data foundation and governance experience with Yidu Tech's medical data governance and AI application capabilities, exploring three priority directions: compliant use of health and medical data, drug clinical trials and medical research services, and training in health data and AI. The lab is positioned as a collaborative innovation platform, and no concrete results have been announced.

Beijing Municipal Health Big Data and Policy Research Center and Yidu Tech signed a cooperation agreement during the 2026 China International Fair for Trade in Services to build a joint laboratory for intelligent clinical research. The two sides combine Beijing's health-data foundation and governance experience with Yidu Tech's medical data governance and AI application capabilities, exploring three priority directions: compliant use of health and medical data, drug clinical trials and medical research services, and training in health data and AI. The lab is positioned as a collaborative innovation platform, and no concrete results have been announced.

This narrative review, based on a structured search of PubMed/MEDLINE, Scopus, and Web of Science, maps the clinical applications of artificial intelligence across otolaryngology subspecialties, noting that deep learning shows potential in sinonasal disease detection, automated image segmentation, lymph node metastasis prediction, thyroid nodule classification, and prognostic modeling in head and neck cancer, while multimodal systems and generative large language models are emerging in medical education, image interpretation, differential diagnosis, and clinical decision support; however, limited external validation, retrospective designs, dataset heterogeneity, algorithmic bias, lack of transparency, privacy concerns, medico-legal uncertainty, and automation bias still constrain broad imp

This narrative review, based on a structured search of PubMed/MEDLINE, Scopus, and Web of Science, maps the clinical applications of artificial intelligence across otolaryngology subspecialties, noting that deep learning shows potential in sinonasal disease detection, automated image segmentation, lymph node metastasis prediction, thyroid nodule classification, and prognostic modeling in head and neck cancer, while multimodal systems and generative large language models are emerging in medical education, image interpretation, differential diagnosis, and clinical decision support; however, limited external validation, retrospective designs, dataset heterogeneity, algorithmic bias, lack of transparency, privacy concerns, medico-legal uncertainty, and automation bias still constrain broad imp